def __init__(self, builder: ConvBuilder, deps):
super(LeNet5, self).__init__()
self.bd = builder
stem = builder.Sequential()
stem.add_module(
'conv1',
builder.Conv2d(in_channels=1,
out_channels=LENET5_DEPS[0],
kernel_size=5,
bias=True))
stem.add_module('maxpool1', builder.Maxpool2d(kernel_size=2))
stem.add_module(
'conv2',
builder.Conv2d(in_channels=LENET5_DEPS[0],
out_channels=LENET5_DEPS[1],
kernel_size=5,
bias=True))
stem.add_module('maxpool2', builder.Maxpool2d(kernel_size=2))
self.stem = stem
self.flatten = builder.Flatten()
self.linear1 = builder.Linear(in_features=LENET5_DEPS[1] * 16,
out_features=LENET5_DEPS[2])
self.relu1 = builder.ReLU()
self.linear2 = builder.Linear(in_features=LENET5_DEPS[2],
out_features=10)
def __init__(self, builder:ConvBuilder, num_classes):
super(MobileV1CifarNet, self).__init__()
self.conv1 = builder.Conv2dBNReLU(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)
blocks = []
in_planes = cifar_cfg[0]
for x in cifar_cfg:
out_planes = x if isinstance(x, int) else x[0]
stride = 1 if isinstance(x, int) else x[1]
blocks.append(MobileV1Block(builder=builder, in_planes=in_planes, out_planes=out_planes, stride=stride))
in_planes = out_planes
self.stem = builder.Sequential(*blocks)
self.gap = builder.GAP(kernel_size=8)
self.linear = builder.Linear(cifar_cfg[-1], num_classes)
def __init__(self, builder:ConvBuilder, deps):
super(LeNet5BN, self).__init__()
self.bd = builder
stem = builder.Sequential()
stem.add_module('conv1', builder.Conv2dBNReLU(in_channels=1, out_channels=deps[0], kernel_size=5))
stem.add_module('maxpool1', builder.Maxpool2d(kernel_size=2))
stem.add_module('conv2', builder.Conv2dBNReLU(in_channels=deps[0], out_channels=deps[1], kernel_size=5))
stem.add_module('maxpool2', builder.Maxpool2d(kernel_size=2))
self.stem = stem
self.flatten = builder.Flatten()
self.linear1 = builder.IntermediateLinear(in_features=deps[1] * 16, out_features=500)
self.relu1 = builder.ReLU()
self.linear2 = builder.Linear(in_features=500, out_features=10)
def __init__(self, builder:ConvBuilder, num_classes, deps=None):
super(MobileV1ImagenetNet, self).__init__()
if deps is None:
deps = MI1_ORIGIN_DEPS
assert len(deps) == 27
self.conv1 = builder.Conv2dBNReLU(in_channels=3, out_channels=deps[0], kernel_size=3, stride=2, padding=1)
blocks = []
for block_idx in range(13):
depthwise_channels = int(deps[block_idx * 2 + 1])
pointwise_channels = int(deps[block_idx * 2 + 2])
stride = 2 if block_idx in [1, 3, 5, 11] else 1
blocks.append(MobileV1Block(builder=builder, in_planes=depthwise_channels, out_planes=pointwise_channels, stride=stride))
self.stem = builder.Sequential(*blocks)
self.gap = builder.GAP(kernel_size=7)
self.linear = builder.Linear(imagenet_cfg[-1], num_classes)
def __init__(self, builder:ConvBuilder, deps=SIMPLE_ALEXNET_DEPS):
super(AlexBN, self).__init__()
# self.bd = builder
stem = builder.Sequential()
stem.add_module('conv1', builder.Conv2dBNReLU(in_channels=3, out_channels=deps[0], kernel_size=11, stride=4, padding=2))
stem.add_module('maxpool1', builder.Maxpool2d(kernel_size=3, stride=2))
stem.add_module('conv2', builder.Conv2dBNReLU(in_channels=deps[0], out_channels=deps[1], kernel_size=5, padding=2))
stem.add_module('maxpool2', builder.Maxpool2d(kernel_size=3, stride=2))
stem.add_module('conv3',
builder.Conv2dBNReLU(in_channels=deps[1], out_channels=deps[2], kernel_size=3, padding=1))
stem.add_module('conv4',
builder.Conv2dBNReLU(in_channels=deps[2], out_channels=deps[3], kernel_size=3, padding=1))
stem.add_module('conv5',
builder.Conv2dBNReLU(in_channels=deps[3], out_channels=deps[4], kernel_size=3, padding=1))
stem.add_module('maxpool3', builder.Maxpool2d(kernel_size=3, stride=2))
self.stem = stem
self.flatten = builder.Flatten()
self.linear1 = builder.Linear(in_features=deps[4] * 6 * 6, out_features=4096)
self.relu1 = builder.ReLU()
self.drop1 = builder.Dropout(0.5)
self.linear2 = builder.Linear(in_features=4096, out_features=4096)
self.relu2 = builder.ReLU()
self.drop2 = builder.Dropout(0.5)
self.linear3 = builder.Linear(in_features=4096, out_features=1000)
def __init__(self, num_classes, builder: ConvBuilder, deps):
super(VANet, self).__init__()
sq = builder.Sequential()
sq.add_module(
'conv1',
builder.Conv2dBNReLU(in_channels=3,
out_channels=deps[0],
kernel_size=3,
stride=1,
padding=1))
sq.add_module(
'conv2',
builder.Conv2dBNReLU(in_channels=deps[0],
out_channels=deps[1],
kernel_size=3,
stride=1,
padding=1))
sq.add_module('maxpool1', builder.Maxpool2d(kernel_size=2))
sq.add_module(
'conv3',
builder.Conv2dBNReLU(in_channels=deps[1],
out_channels=deps[2],
kernel_size=3,
stride=1,
padding=1))
sq.add_module(
'conv4',
builder.Conv2dBNReLU(in_channels=deps[2],
out_channels=deps[3],
kernel_size=3,
stride=1,
padding=1))
sq.add_module('maxpool2', builder.Maxpool2d(kernel_size=2))
sq.add_module(
'conv5',
builder.Conv2dBNReLU(in_channels=deps[3],
out_channels=deps[4],
kernel_size=3,
stride=1,
padding=1))
sq.add_module(
'conv6',
builder.Conv2dBNReLU(in_channels=deps[4],
out_channels=deps[5],
kernel_size=3,
stride=1,
padding=1))
sq.add_module(
'conv7',
builder.Conv2dBNReLU(in_channels=deps[5],
out_channels=deps[6],
kernel_size=3,
stride=1,
padding=1))
sq.add_module('maxpool3', builder.Maxpool2d(kernel_size=2))
sq.add_module(
'conv8',
builder.Conv2dBNReLU(in_channels=deps[6],
out_channels=deps[7],
kernel_size=3,
stride=1,
padding=1))
sq.add_module(
'conv9',
builder.Conv2dBNReLU(in_channels=deps[7],
out_channels=deps[8],
kernel_size=3,
stride=1,
padding=1))
sq.add_module(
'conv10',
builder.Conv2dBNReLU(in_channels=deps[8],
out_channels=deps[9],
kernel_size=3,
stride=1,
padding=1))
sq.add_module('maxpool4', builder.Maxpool2d(kernel_size=2))
sq.add_module(
'conv11',
builder.Conv2dBNReLU(in_channels=deps[9],
out_channels=deps[10],
kernel_size=3,
stride=1,
padding=1))
sq.add_module(
'conv12',
builder.Conv2dBNReLU(in_channels=deps[10],
out_channels=deps[11],
kernel_size=3,
stride=1,
padding=1))
sq.add_module(
'conv13',
builder.Conv2dBNReLU(in_channels=deps[11],
out_channels=deps[12],
kernel_size=3,
stride=1,
padding=1))
sq.add_module('maxpool5', builder.Maxpool2d(kernel_size=2))
self.stem = sq
self.flatten = builder.Flatten()
self.linear1 = builder.IntermediateLinear(in_features=deps[12],
out_features=512)
self.relu = builder.ReLU()
self.linear2 = builder.Linear(in_features=512,
out_features=num_classes)
